Neuroanatomy studies and analyzes the structure of brain regions and cells. Core of classical neuroanatomical studies have always been hand based drawing of neuronal structures of microscopy specimen. These 2-dimensional (2D) drawings are easy to display but present increasing difficulty for any other kind of research since they remove the Z component from the original data.
Neuronal reconstruction is a small sector within Neuroanatomy. One of the reasons of its small size and influence is due to the extreme amount of man-power required to extract data. Typical reconstruction studies are measured in man-years and their results are limited. These huge time requirements with the limited results have constrained the amount of studies performed. At the same time neuronal structures are the basic building block of all brains and multiple fields are already utilizing all available reconstructions. There is a need to reconstruct more neurons from more brains of different species. From these considerations a more time-efficient technique is an overdue need within the neuroanatomical community. If reconstruction outputs will be limited for longer periods than important sectors like electrophysiological simulations, network analysis, and neuroanatomical studies could be affected.
With the advent of modem computers a new push towards 3D neuronal reconstructions has been in place from different sources (Tamamaki et al., 1988; Glaser and Glaser, 1990). Few neuronal techniques are available that produce 3D data like MicroBright Field Neurolucida (available from MBF Bioscience of Williston, Vt.) or template base data (Gras and Killman 1983). Of these techniques only Neurolucida, the most common choice for 3D neuronal reconstructions, generates connected arborizations that allow full 3D analysis. Other less widely used reconstruction techniques are available. One is a template-based system that produces disconnected set of segments (Tamamaki et al., 1988). Another technique uses polynomial interpolation to join 2D reconstruction from serial images (Wolf et al., 1995). Neurolucida represents the current standard in the field and it is installed in many laboratories across the globe. Disadvantages of this technique are its cost, its specialized hardware requirements and the amount of human intervention required to obtain good data. In particular a full system requires an investment of ˜$80 k in specialized hardware and software.
What is needed is an inexpensive and temporally efficient system to digitally reconstruct neuronal arborizations for use in evaluating the structure of brain cells.